TEXAS A&M

Institute of
Data Science

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Curriculum Overview

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Curriculum 

This is a 30-hour program that consists of ten (10) courses.  Students from all tracks will take the same set of four (4) core courses, which develop students with a strong foundation of data science in mathematics, statistics, computing skills, and machine learning.

Students will choose six (6) elective courses from the list of courses offered by five different departments. 

Students typically take all four (4) core courses in the first semester and then take three (3) elective courses in each of the two subsequent semesters. Students are required to take three (3) elective courses from the department of the track into which they are admitted and can take the remainder of their elective courses from any of the departments listed below (including the track into which they were admitted if they wish).

Courses Offered for Current Academic Year

MSDS Core Courses

These core courses are required for all MSDS students:

  • MATH 677 Mathematical Foundations for Data Science (3 Lecture Credits Hours)—Linear systems; least squares problems; eigenvalue decomposition; singular value decomposition; Perron–Frobenius theory; dynamic programming; convex optimization; gradient descent; linear programming; semidefinite programming; compressive sensing.
  • STAT 650 Statistical Foundation For Data Science (3 Lecture Credits Hours)—Introduction to both probability and statistics with emphasis on applications in data science; topics include basic probability concepts, sample space, conditional probability, random variables, as well as statistical inference.
  • STAT 624 Databases and Computational Tools Used in Big Data (3 Lecture Credits Hours)—Survey of common tools used by statisticians for high-performance computing and big data type problems; shell scripting; HPC clusters; code optimization and vectorization; parallelizing applications using numerical libraries; open MP, MPI, and parallel R; data management and revision control using Git; exploration of SQL, survey NOSQL databases; introduction to Python.
  • STAT 639/ECEN 758/CSCE 756 Data Mining and Analysis (3 Lecture Credits Hours)—A broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, clustering, and classification; applications to scientific and online data.

Computer Science and Engineering

Elective courses offered by the Department of Computer Science and Engineering:

  • CSCE 608 Database Systems
  • CSCE 625 Artificial Intelligence
  • CSCE 633 Machine Learning
  • CSCE 636 Deep Learning
  • CSCE 638 Natural Language Processing: Foundations and Techniques
  • CSCE 642 Deep Reinforcement Learning
  • CSCE 666 Pattern Analysis
  • CSCE 670 Information Storage and Retrieval
  • CSCE 671 Computer-Human Interaction
  • CSCE 678 / ECEN 757 Distributed Systems and Cloud Computing
  • CSCE 679/VIZA 676 Data Visualization
  • CSCE 704 / CYBR 604 Data Analytics for Cybersecurity
  • CSCE 735 Parallel Computing

CSCE course descriptions are available online at TAMU’s Graduate and Professional Catalog.

Electrical and Computer Engineering

Elective courses offered by the Department of Electrical and Computer Engineering:

  • ECEN 642 Digital Image Processing & Computer Vision
  • ECEN 644 Discretetime Systems
  • ECEN 649 Pattern Recognition
  • ECEN 663 Data Compression With Applications to Speech and Video
  • ECEN 689 Scientific Machine Learning
  • ECEN 732 Online Decision Making and Learning
  • ECEN 740 Machine Learning Engineering
  • ECEN 743 Reinforcement Learning
  • ECEN 748 Data Stream Algorithms and Applications
  • ECEN 757 / CSCE 678 Distributed Systems & Cloud Computing
  • ECEN 760 Introduction to Probabilistic Graphical Models
  • ECEN 765 Machine Learning with Networks
  • ECEN 766 Algorithms in Structural Bioinformatics
  • ECEN 769 Materials Informatics
  • ECEN 725 / CSCE 725 / STAT 683 Data Science Capstone (counts as elective in any track)

ECEN course descriptions are available online at TAMU’s Graduate and Professional Catalog.

Mathematics

Elective courses offered by the Department of Mathematics:

  • MATH 609 Numerical Analysis
  • MATH 613 Graph Theory
  • MATH 664 Topics in Mathematical Data Science
  • MATH 678 Introduction to Topological Data Analysis
  • MATH 679 Mathematical Algorithms and Their Implementations
  • MATH 680 Topics in Mathematical Data Science
  • MATH 689 Special Topics in Deep Learning: Theory and Application

Math course descriptions are available online at TAMU’s Graduate and Professional Catalog.

Statistics

Elective courses offered by the Department of Statistics:

  • STAT 608 Regression Analytics
  • STAT 616 Statistical Aspects of Machine Learning I
  • STAT 618 Statistical Aspects of Machine Learning II
  • STAT 626 Methods in Time Series Analysis
  • STAT 636 Applied Multivariate Analysis and Statistical Learning
  • STAT 638 Applied Bayesian Analysis
  • STAT 645 Applied Biostatistics and Data Analysis
  • STAT 646 Statistical Bioinformatics
  • STAT 647 Applied Spatial Statistics
  • STAT 654 Statistical Computing with R and Python
  • STAT 656 Applied Analytics
  • STAT 659 Applied Categorical Data Analysis

Statistics course descriptions are available online at TAMU’s Graduate and Professional Catalog.

Industrial and System Engineering

Elective courses offered by the Department of Industrial and System Engineering:

  • ISEN 613 Engineering Data Analysis
  • ISEN 619 Analysis & Prediction

Statistics course descriptions are available online at TAMU’s Graduate and Professional Catalog.